Machine learning-driven dynamic prediction and optimization for ammonia recovery in membrane distillation system.

Area of Science:

• Environmental Engineering
• Chemical Engineering
• Data Science

Background:

• Ammonia recovery is vital for environmental protection and resource sustainability.
• Membrane distillation (MD) offers a path for high-purity ammonia recovery, but its optimization requires understanding dynamic transport.
• Existing models struggle with complex system dynamics and generalization.

Purpose of the Study:

• To develop a flexible and accurate model for ammonia transport in MD systems.
• To integrate machine learning (ML) with dynamic simulation for improved process modeling.
• To enable real-time process control and adaptive optimization of ammonia recovery.

Main Methods:

• Theoretical analysis identified key variables: temperature, pH, and ammonia partial pressure gradient.
• An artificial neural network (ANN) was developed to simulate the rate of ammonia concentration change.
• The ANN was coupled with the fourth-order Runge-Kutta (RK4) algorithm for real-time predictions.

Main Results:

• The ANN model achieved high accuracy (R² = 0.8537) in predicting ammonia concentration changes.
• The integrated model successfully predicted real-time ammonia concentrations and cumulative recovery rates.
• ML-based grid predictions identified optimal operational parameters for instantaneous MD performance.

Conclusions:

• This hybrid approach enhances understanding of ammonia transport mechanisms in MD.
• The study advances real-time process control and adaptive optimization for ammonia recovery.
• The findings promote both theoretical insights and practical applications in resource sustainability.